{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zX4Kg8DUTKWO"
   },
   "outputs": [],
   "source": [
    "# 模拟生成时间序列\n",
    "# 模拟生成数据集\n",
    "# 使用神经网络预测时间序列\n",
    "\n",
    "\n",
    "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "# https://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/lmoroney/dlaicourse/blob/master/TensorFlow%20In%20Practice/Course%204%20-%20S%2BP/S%2BP%20Week%202%20Lesson%202.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-pwam8szRReT"
   },
   "outputs": [],
   "source": [
    "try:\n",
    "  # %tensorflow_version only exists in Colab.\n",
    "  %tensorflow_version 2.x\n",
    "except Exception:\n",
    "  pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BOjujz601HcS"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.2.0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Zswl7jRtGzkk"
   },
   "outputs": [],
   "source": [
    "# 1. 模拟生成时间序列\n",
    "def plot_series(time, series, format=\"-\", start=0, end=None):\n",
    "    plt.plot(time[start:end], series[start:end], format)\n",
    "    plt.xlabel(\"Time\")\n",
    "    plt.ylabel(\"Value\")\n",
    "    plt.grid(True)\n",
    "\n",
    "def trend(time, slope=0):\n",
    "    return slope * time\n",
    "\n",
    "def seasonal_pattern(season_time):\n",
    "    \"\"\"Just an arbitrary pattern, you can change it if you wish\"\"\"\n",
    "    return np.where(season_time < 0.4,\n",
    "                    np.cos(season_time * 2 * np.pi),\n",
    "                    1 / np.exp(3 * season_time))\n",
    "\n",
    "def seasonality(time, period, amplitude=1, phase=0):\n",
    "    \"\"\"Repeats the same pattern at each period\"\"\"\n",
    "    season_time = ((time + phase) % period) / period\n",
    "    return amplitude * seasonal_pattern(season_time)\n",
    "\n",
    "def noise(time, noise_level=1, seed=None):\n",
    "    rnd = np.random.RandomState(seed)\n",
    "    return rnd.randn(len(time)) * noise_level\n",
    "\n",
    "time = np.arange(4 * 365 + 1, dtype=\"float32\")\n",
    "baseline = 10\n",
    "series = trend(time, 0.1)  \n",
    "baseline = 10\n",
    "amplitude = 40\n",
    "slope = 0.05\n",
    "noise_level = 5\n",
    "\n",
    "# Create the series\n",
    "series = baseline + trend(time, slope) + seasonality(time, period=365, amplitude=amplitude)\n",
    "# Update with noise\n",
    "series += noise(time, noise_level, seed=42)\n",
    "\n",
    "split_time = 1000\n",
    "time_train = time[:split_time]\n",
    "x_train = series[:split_time]\n",
    "time_valid = time[split_time:]\n",
    "x_valid = series[split_time:]\n",
    "\n",
    "window_size = 20\n",
    "batch_size = 32\n",
    "shuffle_buffer_size = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4sTTIOCbyShY"
   },
   "outputs": [],
   "source": [
    "# 模拟生成数据集\n",
    "def windowed_dataset(series, window_size, batch_size, shuffle_buffer):\n",
    "  dataset = tf.data.Dataset.from_tensor_slices(series)\n",
    "  dataset = dataset.window(window_size + 1, shift=1, drop_remainder=True)\n",
    "  dataset = dataset.flat_map(lambda window: window.batch(window_size + 1))\n",
    "  dataset = dataset.shuffle(shuffle_buffer).map(lambda window: (window[:-1], window[-1]))\n",
    "  dataset = dataset.batch(batch_size).prefetch(1)\n",
    "  return dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ou-WmE2AXu6B"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<PrefetchDataset shapes: ((None, None), (None,)), types: (tf.float32, tf.float32)>\n",
      "Layer weights [array([[ 1.89684648e-02],\n",
      "       [-8.18130746e-02],\n",
      "       [ 5.79490028e-02],\n",
      "       [-6.66455030e-02],\n",
      "       [ 1.01574466e-01],\n",
      "       [ 1.86705515e-02],\n",
      "       [-1.66844428e-02],\n",
      "       [-6.37873933e-02],\n",
      "       [-6.45572506e-03],\n",
      "       [ 6.24558665e-02],\n",
      "       [-2.67585763e-03],\n",
      "       [ 4.11496905e-04],\n",
      "       [-1.07755788e-01],\n",
      "       [ 1.69288516e-01],\n",
      "       [-2.75158547e-02],\n",
      "       [ 4.55670804e-02],\n",
      "       [-2.54287459e-02],\n",
      "       [ 1.69430181e-01],\n",
      "       [ 2.70241588e-01],\n",
      "       [ 4.88252103e-01]], dtype=float32), array([0.01520508], dtype=float32)]\n"
     ]
    }
   ],
   "source": [
    "# 使用单层神经网络预测时间序列\n",
    "\n",
    "dataset = windowed_dataset(x_train, window_size, batch_size, shuffle_buffer_size)\n",
    "print(dataset)\n",
    "l0 = tf.keras.layers.Dense(1, input_shape=[window_size])\n",
    "model = tf.keras.models.Sequential([l0])\n",
    "\n",
    "model.compile(loss=\"mse\", optimizer=tf.keras.optimizers.SGD(lr=1e-6, momentum=0.9))\n",
    "model.fit(dataset,epochs=100,verbose=0)\n",
    "\n",
    "print(\"Layer weights {}\".format(l0.get_weights()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-gtVJuLVxR-M"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "forecast = []\n",
    "\n",
    "for time in range(len(series) - window_size):\n",
    "  forecast.append(model.predict(series[time:time + window_size][np.newaxis]))\n",
    "\n",
    "forecast = forecast[split_time-window_size:]\n",
    "results = np.array(forecast)[:, 0, 0]\n",
    "\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "plot_series(time_valid, x_valid)\n",
    "plot_series(time_valid, results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hR2BO0Dai_ZT"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.091783"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算绝对误差\n",
    "tf.keras.metrics.mean_absolute_error(x_valid, results).numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "include_colab_link": true,
   "name": "S+P Week 2 Lesson 2.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
